is proposed for training acceleration by reducing communication cost of weight gradients exchanging in distributed learning system.
Aji [1]} prunes \(99\%\)  weight gradients with the smallest absolute value by a heuristic algorithm.
According to filters’ correlationship, Prakash [2]} prunes \(30\%\)  filters temporarily to improve training efficiency.
